Data Analytics Engineer
What you will be doing
- Analytics Development: Design, develop, and deploy interactive analytics applications using Streamlit in Snowflake. Create dashboards and reports in Tableau and PowerBI to visualize data trends and insights.
- Trending Analytics: Analyze large datasets to identify trends, patterns, and insights that can drive business decisions. Develop predictive models and algorithms to forecast future trends.
- Data Visualization: Create compelling visualizations that communicate complex data insights in an understandable and actionable manner. Ensure visualizations are user-friendly and meet the needs of various stakeholders.
- Collaboration: Work closely with cross-functional teams, including data scientists, business analysts, and IT, to understand business requirements and translate them into technical solutions.
- Performance Optimization: Optimize analytics applications and dashboards for performance and scalability. Ensure data accuracy and integrity in all analytics outputs.
- Stakeholder Engagement: Present findings and insights to stakeholders at various levels of the organization. Provide recommendations based on data analysis to support strategic decision-making.
- Continuous Improvement: Stay updated with the latest trends and best practices in analytics, data visualization, and business intelligence. Continuously seek opportunities to improve analytics processes and tools.
- Trending Analytics: Analyze large datasets to identify trends, patterns, and insights that can drive business decisions. Develop predictive models and algorithms to forecast future trends.
- Data Visualization: Create compelling visualizations that communicate complex data insights in an understandable and actionable manner. Ensure visualizations are user-friendly and meet the needs of various stakeholders.
- Collaboration: Work closely with cross-functional teams, including data scientists, business analysts, and IT, to understand business requirements and translate them into technical solutions.
- Performance Optimization: Optimize analytics applications and dashboards for performance and scalability. Ensure data accuracy and integrity in all analytics outputs.
- Stakeholder Engagement: Present findings and insights to stakeholders at various levels of the organization. Provide recommendations based on data analysis to support strategic decision-making.
- Continuous Improvement: Stay updated with the latest trends and best practices in analytics, data visualization, and business intelligence. Continuously seek opportunities to improve analytics processes and tools.